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Open AccessArticle

Kalman-Filter-Based Orientation Determination Using Inertial/Magnetic Sensors: Observability Analysis and Performance Evaluation

The BioRobotics Institute, Scuola Superiore Sant’Anna, Piazza Martiri della Libertà 33, 56124 Pisa, Italy
Sensors 2011, 11(10), 9182-9206; https://doi.org/10.3390/s111009182
Received: 4 August 2011 / Revised: 21 September 2011 / Accepted: 23 September 2011 / Published: 27 September 2011
(This article belongs to the Section Physical Sensors)
In this paper we present a quaternion-based Extended Kalman Filter (EKF) for estimating the three-dimensional orientation of a rigid body. The EKF exploits the measurements from an Inertial Measurement Unit (IMU) that is integrated with a tri-axial magnetic sensor. Magnetic disturbances and gyro bias errors are modeled and compensated by including them in the filter state vector. We employ the observability rank criterion based on Lie derivatives to verify the conditions under which the nonlinear system that describes the process of motion tracking by the IMU is observable, namely it may provide sufficient information for performing the estimation task with bounded estimation errors. The observability conditions are that the magnetic field, perturbed by first-order Gauss-Markov magnetic variations, and the gravity vector are not collinear and that the IMU is subject to some angular motions. Computer simulations and experimental testing are presented to evaluate the algorithm performance, including when the observability conditions are critical. View Full-Text
Keywords: ambulatory human motion tracking; orientation determination; inertial measurement unit; Extended Kalman filter; Lie derivatives; observability of nonlinear systems ambulatory human motion tracking; orientation determination; inertial measurement unit; Extended Kalman filter; Lie derivatives; observability of nonlinear systems
MDPI and ACS Style

Sabatini, A.M. Kalman-Filter-Based Orientation Determination Using Inertial/Magnetic Sensors: Observability Analysis and Performance Evaluation. Sensors 2011, 11, 9182-9206.

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